Evidence for a neural model to evaluate symmetry in V1
| Main Author: | |
|---|---|
| Publication Date: | 2010 |
| Other Authors: | |
| Format: | Conference object |
| Language: | eng |
| Source: | Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) |
| Download full: | http://hdl.handle.net/10174/6910 |
Summary: | 50 years ago Hubel and Wiesel discovered simple and complex cells in V1, but there is still no consensus on their functional roles. It is agreed that complex cells are more often selective for direction of motion than simple cells, that there are differences in the way they combine information within their receptive fields, and that complex cells probably receive most of their input from simple cells, but what this serial hierarchy achieves is not understood. There is another puzzling dichotomy that we think is related, namely that of cross-correlation, which is widely accepted as the operation performed on the input image by simple cells, and auto-correlation, which some think underlies the perception of Glass patterns, and possibly motion. We propose the hypothesis that complex cells signal auto-correlations in the visual image, but to evaluate them they require the preliminary analysis done by simple cells, and also pinwheels - structures intervening between simple cells and complex cells that were quite unknown to Hubel and Wiesel. We shall first present psychophysical evidence, using a new kind of random dot display, which suggests that both cross-correlation and auto-correlation are performed in early vision. We then point to recent evidence on the micro-circuitry of pinwheels, and mappings of their intrinsic activity, which shows how pinwheels might enable complex cells to respond selectively to autocorrelations in the input image that activates the simple cells. Auto-correlation is a powerful tool for detecting symmetry, and many may be surprised by evidence that such an abstract property is detected so early in visual perception. |
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Evidence for a neural model to evaluate symmetry in V1VisionVisual processing50 years ago Hubel and Wiesel discovered simple and complex cells in V1, but there is still no consensus on their functional roles. It is agreed that complex cells are more often selective for direction of motion than simple cells, that there are differences in the way they combine information within their receptive fields, and that complex cells probably receive most of their input from simple cells, but what this serial hierarchy achieves is not understood. There is another puzzling dichotomy that we think is related, namely that of cross-correlation, which is widely accepted as the operation performed on the input image by simple cells, and auto-correlation, which some think underlies the perception of Glass patterns, and possibly motion. We propose the hypothesis that complex cells signal auto-correlations in the visual image, but to evaluate them they require the preliminary analysis done by simple cells, and also pinwheels - structures intervening between simple cells and complex cells that were quite unknown to Hubel and Wiesel. We shall first present psychophysical evidence, using a new kind of random dot display, which suggests that both cross-correlation and auto-correlation are performed in early vision. We then point to recent evidence on the micro-circuitry of pinwheels, and mappings of their intrinsic activity, which shows how pinwheels might enable complex cells to respond selectively to autocorrelations in the input image that activates the simple cells. Auto-correlation is a powerful tool for detecting symmetry, and many may be surprised by evidence that such an abstract property is detected so early in visual perception.Front. Neurosci. Conference Abstract: Computational and Systems Neuroscience 20102012-12-21T12:23:31Z2012-12-212010-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObjecthttp://hdl.handle.net/10174/6910http://hdl.handle.net/10174/6910engBarlow, H.B. & Berry, D.L. Evidence for a Neural Model to Evaluate Symmetry in V1. Computational and Systems Neuroscience 2010.http://www.frontiersin.org/Community/AbstractDetails.aspx?ABS_DOI=10.3389/conf.fnins.2010.03.00038&eid=770&sname=Computational_and_Systems_Neuroscience_2010simnaonaonddberry@uevora.pt360Barlow, Horace B.Berry, David L.info:eu-repo/semantics/openAccessreponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiainstacron:RCAAP2024-01-03T18:46:37Zoai:dspace.uevora.pt:10174/6910Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-28T11:56:49.315555Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiafalse |
| dc.title.none.fl_str_mv |
Evidence for a neural model to evaluate symmetry in V1 |
| title |
Evidence for a neural model to evaluate symmetry in V1 |
| spellingShingle |
Evidence for a neural model to evaluate symmetry in V1 Barlow, Horace B. Vision Visual processing |
| title_short |
Evidence for a neural model to evaluate symmetry in V1 |
| title_full |
Evidence for a neural model to evaluate symmetry in V1 |
| title_fullStr |
Evidence for a neural model to evaluate symmetry in V1 |
| title_full_unstemmed |
Evidence for a neural model to evaluate symmetry in V1 |
| title_sort |
Evidence for a neural model to evaluate symmetry in V1 |
| author |
Barlow, Horace B. |
| author_facet |
Barlow, Horace B. Berry, David L. |
| author_role |
author |
| author2 |
Berry, David L. |
| author2_role |
author |
| dc.contributor.author.fl_str_mv |
Barlow, Horace B. Berry, David L. |
| dc.subject.por.fl_str_mv |
Vision Visual processing |
| topic |
Vision Visual processing |
| description |
50 years ago Hubel and Wiesel discovered simple and complex cells in V1, but there is still no consensus on their functional roles. It is agreed that complex cells are more often selective for direction of motion than simple cells, that there are differences in the way they combine information within their receptive fields, and that complex cells probably receive most of their input from simple cells, but what this serial hierarchy achieves is not understood. There is another puzzling dichotomy that we think is related, namely that of cross-correlation, which is widely accepted as the operation performed on the input image by simple cells, and auto-correlation, which some think underlies the perception of Glass patterns, and possibly motion. We propose the hypothesis that complex cells signal auto-correlations in the visual image, but to evaluate them they require the preliminary analysis done by simple cells, and also pinwheels - structures intervening between simple cells and complex cells that were quite unknown to Hubel and Wiesel. We shall first present psychophysical evidence, using a new kind of random dot display, which suggests that both cross-correlation and auto-correlation are performed in early vision. We then point to recent evidence on the micro-circuitry of pinwheels, and mappings of their intrinsic activity, which shows how pinwheels might enable complex cells to respond selectively to autocorrelations in the input image that activates the simple cells. Auto-correlation is a powerful tool for detecting symmetry, and many may be surprised by evidence that such an abstract property is detected so early in visual perception. |
| publishDate |
2010 |
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2010-01-01T00:00:00Z 2012-12-21T12:23:31Z 2012-12-21 |
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info:eu-repo/semantics/publishedVersion |
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info:eu-repo/semantics/conferenceObject |
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http://hdl.handle.net/10174/6910 http://hdl.handle.net/10174/6910 |
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eng |
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eng |
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Barlow, H.B. & Berry, D.L. Evidence for a Neural Model to Evaluate Symmetry in V1. Computational and Systems Neuroscience 2010. http://www.frontiersin.org/Community/AbstractDetails.aspx?ABS_DOI=10.3389/conf.fnins.2010.03.00038&eid=770&sname=Computational_and_Systems_Neuroscience_2010 sim nao nao nd dberry@uevora.pt 360 |
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openAccess |
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Front. Neurosci. Conference Abstract: Computational and Systems Neuroscience 2010 |
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Front. Neurosci. Conference Abstract: Computational and Systems Neuroscience 2010 |
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